Biology subjects

Youngblut, N. D.

Publications and source records attributed to Youngblut, N. D..

27 records · Page 2Linked to original sources

bioRxiv2020

Incorporating genome-based phylogeny and trait similarity into diversity assessments helps to resolve a global collection of human gut metagenomes

Tree-based diversity measures incorporate phylogenetic or functional relatedness into comparisons of microbial communities. This can improve the identification of explanatory factors compared to tree-agnostic diversity measures. However, applying tree-based diversity measures to metagenome data is more challenging than for single-locus sequencing (e.g., 16S rRNA gene). The Genome Taxonomy Database (GTDB) provides a genome-based reference database that can be used for species-level metagenome profiling, and a multi-locus phylogeny of all genomes that can be employed for diversity calculations. This approach also allows for functional diversity measures based on genomic content or traits inferred from it. Still, it is unclear how metagenome-based assessments of microbiome diversity benefit from incorporating phylogeny or function into measures of diversity. We assessed this by measuring phylogeny-based, function-based, and tree-agnostic diversity measures from a large, global collection of human gut metagenomes composed of 33 studies and 3348 samples. We found tree-based measures to explain phenotypic variation (e.g., westernization, disease status, and gender) better or on par with tree-agnostic measures. Ecophylogenetic and functional diversity measures provided unique insight into how microbiome diversity was partitioned by phenotype. Tree-based measures greatly improved machine learning model performance for predicting westernization, disease status, and gender, relative to models trained solely on tree-agnostic measures. Notably, ecophylogenetic and functional diversity measures were generally the most important features for predictive performance. Our findings illustrate the usefulness of tree- and function-based measures for metagenomic assessments of microbial diversity - a fundamental component of microbiome science. ImportanceEstimations of microbiome diversity are fundamental to understanding spatiotemporal changes of microbial communities and identifying which factors mediate such changes. Tree-based measures of diversity, which consider species relatedness, are widespread for amplicon-based microbiome studies due to their utility relative to tree-agnostic measures. However, tree-based measures are seldomly applied to shotgun metagenomics data. We evaluated the utility of phylogeny, functional relatedness, and tree-agnostic diversity measures on a large scale human gut metagenome dataset to help guide researchers with the complex task of evaluating microbiome diversity via metagenomics.

bioinformatics↗

bioRxiv2020

Large scale metagenome assembly reveals novel animal-associated microbial genomes, biosynthetic gene clusters, and other genetic diversity

Large-scale metagenome assemblies of human microbiomes have produced a vast catalogue of previously unseen microbial genomes; however, comparatively few microbial genomes derive from other vertebrates. Here, we generated 5596 metagenome-assembled genomes from the gut metagenomes of 180 predominantly wild animal species representing 5 classes, in addition to 14 existing animal gut metagenome datasets. The MAGs comprised 1522 species-level genome bins (SGBs); most of which were novel at the species, genus, or family levels, and the majority were enriched in host versus environment metagenomes. Many traits distinguished SGBs enriched in host or environmental biomes, including the number of antimicrobial resistance genes. We identified 1986 diverse biosynthetic gene clusters; only 23 clustered with any MIBiG database references. Gene-based assembly revealed tremendous gene diversity, much of it host- or environment-specific. Our MAG and gene datasets greatly expand the microbial genome repertoire and provide a broad view of microbial adaptations to the vertebrate gut. ImportanceMicrobiome studies on a select few mammalian species (e.g., humans, mice, and cattle) have revealed a great deal of novel genomic diversity in the gut microbiome. However, little is known of the microbial diversity in the gut of other vertebrates. We studied the gut microbiome of a large set of mostly wild animal species consisting of mammals, birds, reptiles, amphibians, and fish. Unfortunately, we found that existing reference databases commonly used for metagenomic analyses failed to capture the microbiome diversity among vertebrates. To increase database representation, we applied advanced metagenome assembly methods to our animal gut data and to many public gut metagenome datasets that had not been used to obtain microbial genomes. Our resulting genome and gene cluster collections comprised a great deal of novel taxonomic and genomic diversity, which we extensively characterized. Our findings substantially expand what is known of microbial genomic diversity in the vertebrate gut.

microbiology↗

bioRxiv2019

Syntrophy via interspecies H2 transfer between Christensenella and Methanobrevibacter underlies their global co-occurrence in the human gut

Across human populations, 16S rRNA gene-based surveys of gut microbiomes have revealed that the bacterial family Christensenellaceae and the archaeal family Methanobacteriaceae co-occur and are enriched in individuals with a lean, compared to an obese, BMI. Whether these association patterns reflect interactions between metabolic partners remains to be ascertained, as well as whether these associations play a role in the lean host phenotype with which they associate. Here, we validated previously reported co-occurrence patterns of the two families, and their association with a lean BMI, with a meta-analysis of 1,821 metagenomes derived from 10 independent studies. Furthermore, we report positive associations at the genus and species level between Christensenella spp. and Methanobrevibacter smithii, the most abundant methanogen of the human gut. By co-culturing three Christensenella spp. With M. smithii, we show that Christensenella spp. efficiently support the of M. smithii via H2 production, far better than Bacteroides thetaiotaomicron. C. minuta forms flocs colonized by M. smithii even when H2 is in excess. In culture with C. minuta, H2 consumption by M. smithii shifts the metabolic output of C. minutas fermentation towards acetate rather than butyrate. Together, these results indicate that the widespread co-occurrence of these microbiota is underpinned by both physical and metabolic interactions. Their combined metabolic activity may provide insights into their association with a lean host BMI. ImportanceThe human gut microbiome is made of trillions of microbial cells, most of which are Bacteria, with a subset of Archaea. The bacterial family Christensenellaceae and the archaeal family Methanobacteriaceae are widespread in human guts. They correlate with each other and with a lean body type. Whether species of these two families interact, and how they affect the body type, are unanswered questions. Here, we showed that species within these families correlate with each other across people. We also demonstrated that particular species of these two families grow together in dense flocs, wherein the bacteria provide hydrogen gas to the archaea, which then make methane. When the archaea are present, the ratio of bacterial products (which are nutrients for humans) is changed. These observations indicate when these species grow together, their products have the potential to affect the physiology of their human host.

microbiology↗

bioRxiv2019

Struo: a pipeline for building custom databases for common metagenome profilers

SummaryTaxonomic and functional information from microbial communities can be efficiently obtained by metagenome profiling, which requires databases of genes and genomes to which sequence reads are mapped. However, the databases that accompany metagenome profilers are not updated at a pace that matches the increase in available microbial genomes. To address this, we developed Struo, a modular pipeline that automatizes the acquisition of genomes from public repositories and the construction of custom databases for multiple metagenome profilers. The use of custom databases that broadly represent the known microbial diversity by incorporating novel genomes results in a substantial increase in mappability of reads in synthetic and real metagenome datasets.\n\nAvailability and implementationSource code available for download at https://github.com/leylabmpi/Struo. Custom GTDB databases available at http://ftp.tue.mpg.de/ebio/projects/struo/\n\nContactnicholas.youngblut@tuebingen.mpg.de

bioinformatics↗

bioRxiv2019

DeepMAsED: Evaluating the quality of metagenomic assemblies

Motivation/backgroundMethodological advances in metagenome assembly are rapidly increasing in the number of published metagenome assemblies. However, identifying misassemblies is challenging due to a lack of closely related reference genomes that can act as pseudo ground truth. Existing reference-free methods are no longer maintained, can make strong assumptions that may not hold across a diversity of research projects, and have not been validated on large scale metagenome assemblies.\n\nResultsWe present DeepMAsED, a deep learning approach for identifying misassembled contigs without the need for reference genomes. Moreover, we provide an in silico pipeline for generating large-scale, realistic metagenome assemblies for comprehensive model training and testing. DeepMAsED accuracy substantially exceeds the state-of-the-art when applied to large and complex metagenome assemblies. Our model estimates close to a 5% contig misassembly rate in two recent large-scale metagenome assembly publications.\n\nConclusionsDeepMAsED accurately identifies misassemblies in metagenome-assembled contigs from a broad diversity of bacteria and archaea without the need for reference genomes or strong modelling assumptions. Running DeepMAsED is straight-forward, as well as is model re-training with our dataset generation pipeline. Therefore, DeepMAsED is a flexible misassembly classifier that can be applied to a wide range of metagenome assembly projects.\n\nAvailabilityDeepMAsED is available from GitHub at https://github.com/leylabmpi/DeepMAsED.

bioinformatics↗

bioRxiv2018

Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades

Multiple factors modulate microbial community assembly in the gut, but the magnitude of each can vary substantially across studies. This may be in part due to a heavy reliance on captive animals, which can have very different gut microbiomes versus their wild counterparts. In order to better resolve the influence of evolution and diet on gut microbiome diversity, we generated a large and highly diverse animal distal gut 16S rRNA microbiome dataset, which comprises 80 % wild animals and includes members of Mammalia, Aves, Reptilia, Amphibia, and Actinopterygii. We decoupled the effects of host evolutionary history and diet on gut microbiome diversity and show that each factor explains different aspects of diversity. Moreover, we resolved particular microbial taxa associated with host phylogeny or diet, and we show that Mammalia have a stronger signal of cophylogeny versus non-mammalian hosts. Additionally, our results from ecophylogenetics and co-occurrence analyses suggest that environmental filtering and microbe-microbe interactions differ among host clades. These findings provide a robust assessment of the processes driving microbial community assembly in the vertebrate intestine.

microbiology↗

bioRxiv2018

Human salivary amylase gene copy number impacts oral and gut microbiomes

Host genetic variation influences the composition of the human microbiome. While studies have focused on associations between the microbiome and single nucleotide polymorphisms in genes, their copy number (CN) can also vary. Here, in a study of human subjects including a 2-week standard diet, we relate oral and gut microbiome to CN at the AMY1 locus, which encodes the gene for salivary amylase, active in starch degradation. We show that although diet standardization drove gut microbiome convergence, AMY1-CN influenced oral and gut microbiome composition and function. The gut microbiomes of low-AMY1-CN subjects had an enhanced capacity for breakdown of complex carbohydrates. Those of high-AMY1 subjects were enriched in microbiota linked to resistant starch fermentation, had higher fecal SCFAs, and drove higher adiposity when transferred to germfree mice. Gut microbiota results were validated in a larger separate population. This study establishes AMY1-CN as a genetic factor patterning microbiome composition and function.

genetics↗

bioRxiv2017

HTSSIP: an R package for analysis of high throughput sequencing data from nucleic acid stable isotope probing (SIP) experiments

Combining high throughput sequencing with stable isotope probing (HTS-SIP) is a powerful method for mapping in situ metabolic processes to thousands of microbial taxa. However, accurately mapping metabolic processes to taxa is complex and challenging. Multiple HTS-SIP data analysis methods have been developed, including high-resolution stable isotope probing (HR-SIP), multi-window high-resolution stable isotope probing (MW-HR-SIP), quantitative stable isotope probing (q-SIP), and {Delta}BD. Currently, the computational tools to perform these analyses are either not publicly available or lack documentation, testing, and developer support. To address this shortfall, we have developed the HTSSIP R package, a toolset for conducting HTS-SIP analyses in a straightforward and easily reproducible manner. The HTSSIP package, along with full documentation and examples, is available from CRAN at https://cran.r-project.org/web/packages/HTSSIP/index.html and Github at https://github.com/nick-youngblut/HTSSIP.

bioinformatics↗

bioRxiv2017

Evaluating The Accuracy Of DNA Stable Isotope Probing

Originality-Significance StatementBy combining DNA Stable Isotope Probing (DNA-SIP) with multiplexed high throughput DNA sequencing (HTS-DNA-SIP), it is now possible to identify patterns of isotope incorporation for thousands of microbial taxa. HTS-DNA-SIP has enormous potential to reveal patterns of carbon and nitrogen exchange within microbial food webs. A current limitation is that, due to the expense of these experiments, it has been impossible to evaluate the accuracy of DNA-SIP methods. We have developed a model that simulates DNA-SIP data, and we use the model to systematically evaluate and validate the accuracy of DNA-SIP analyses. This model can determine the analytical accuracy of DNA-SIP experiments in a range of contexts. Furthermore, the ability to predict experimental outcomes, as a function of experimental design and community characteristics, should be of great use in the design and interpretation DNA-SIP experiments.\n\nSummaryDNA Stable isotope probing (DNA-SIP) is a powerful method that identifies in situ isotope assimilation by microbial taxa. Combining DNA-SIP with multiplexed high throughput DNA sequencing (HTS-DNA-SIP) creates the potential to map in situ assimilation dynamics for thousands of microbial taxonomic units. However, the accuracy of methods for analyzing DNA-SIP data has never been evaluated. We have developed a toolset (SIPSim) for simulating HTS-DNA-SIP datasets and evaluating the accuracy of methods for analyzing HTS-DNA-SIP data. We evaluated two different approaches to analyzing HTS-DNA-SIP data: \"high resolution stable isotope probing\" (HR-SIP) and \"quantitative stable isotope probing\" (q-SIP). HR-SIP was highly specific and moderately sensitive, with very few false positives but potential for false negatives. In contrast, q-SIP had fewer false negatives but many false positives. We also found HR-SIP more robust than q-SIP with respect to experimental variance. Furthermore, we found that the detection sensitivity of HTS-DNA-SIP can be increased without compromising specificity by evaluating evidence of isotope incorporation over multiple windows of buoyant density (MW-HR-SIP). SIPSim provides a platform for determining the accuracy of HTS-DNA-SIP methods across a range of experimental parameters, which will be useful in the design, analysis, and validation of DNA-SIP experiments.

microbiology↗